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| Format: | Preprint |
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2026
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| Online-Zugang: | https://arxiv.org/abs/2605.15766 |
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| _version_ | 1866911688196882432 |
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| author | Li, Loka Zhang, Duzhen Du, Xingbo Song, Leonard Wang, Zixiao Aukenov, Assanali Thomas, Noel Sailaukan, Shakhnazar Yang, Yonghan Chen, Feilong Dong, Jiahua Zhang, Kun Zhang, Bin Song, Le |
| author_facet | Li, Loka Zhang, Duzhen Du, Xingbo Song, Leonard Wang, Zixiao Aukenov, Assanali Thomas, Noel Sailaukan, Shakhnazar Yang, Yonghan Chen, Feilong Dong, Jiahua Zhang, Kun Zhang, Bin Song, Le |
| contents | Large language model (LLM) agents are increasingly capable of automating components of machine learning development, yet existing biomedical benchmarks mainly focus on question answering, reasoning, and tool usage, or evaluate only narrow aspects of biomedical ML coding. We present BioXArena, a biomedical machine learning benchmark designed to evaluate whether agents can generate task-specific model training pipelines for heterogeneous and multi-modal biomedical datasets. BioXArena contains 76 end-to-end tasks across 9 domains, including sequence modeling, single-cell analysis, structural biology, network biology, chemical biology, perturbation dynamics, phenotype-disease modeling, biomedical imaging, and text-integrated learning. Each task is curated from primary biomedical sources into a unified evaluation framework with hidden labels, held-out graders, and biology-aware metrics normalized to a 0 to 1 scale. Agents are required to write executable code, train predictive models, and generate submissions for private test samples. Most tasks involve multiple input modalities, including tabular data, images, natural language, molecular sequences, omics matrices, and protein structures. We evaluate 11 agent configurations in a standardized 2-hour single-GPU environment. MLEvolve with Gemini-3.1-Pro achieves the highest average score of 0.666, followed by GPT-5.4 with 0.636, while no single agent consistently dominates across all domains. We additionally perform extensive ablation studies, robustness evaluations, scaling analyses, cost analyses, and failure-mode investigations to better understand how model backbones, agent scaffolds, inference budgets, and biomedical domains influence BioML coding performance. We will publicly release all benchmark tasks, graders, execution runners, leaderboard results, and agent trajectories. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2605_15766 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | BioXArena: Benchmarking LLM Agents on Multi-Modal Biomedical Machine Learning Tasks Li, Loka Zhang, Duzhen Du, Xingbo Song, Leonard Wang, Zixiao Aukenov, Assanali Thomas, Noel Sailaukan, Shakhnazar Yang, Yonghan Chen, Feilong Dong, Jiahua Zhang, Kun Zhang, Bin Song, Le Computational Engineering, Finance, and Science Large language model (LLM) agents are increasingly capable of automating components of machine learning development, yet existing biomedical benchmarks mainly focus on question answering, reasoning, and tool usage, or evaluate only narrow aspects of biomedical ML coding. We present BioXArena, a biomedical machine learning benchmark designed to evaluate whether agents can generate task-specific model training pipelines for heterogeneous and multi-modal biomedical datasets. BioXArena contains 76 end-to-end tasks across 9 domains, including sequence modeling, single-cell analysis, structural biology, network biology, chemical biology, perturbation dynamics, phenotype-disease modeling, biomedical imaging, and text-integrated learning. Each task is curated from primary biomedical sources into a unified evaluation framework with hidden labels, held-out graders, and biology-aware metrics normalized to a 0 to 1 scale. Agents are required to write executable code, train predictive models, and generate submissions for private test samples. Most tasks involve multiple input modalities, including tabular data, images, natural language, molecular sequences, omics matrices, and protein structures. We evaluate 11 agent configurations in a standardized 2-hour single-GPU environment. MLEvolve with Gemini-3.1-Pro achieves the highest average score of 0.666, followed by GPT-5.4 with 0.636, while no single agent consistently dominates across all domains. We additionally perform extensive ablation studies, robustness evaluations, scaling analyses, cost analyses, and failure-mode investigations to better understand how model backbones, agent scaffolds, inference budgets, and biomedical domains influence BioML coding performance. We will publicly release all benchmark tasks, graders, execution runners, leaderboard results, and agent trajectories. |
| title | BioXArena: Benchmarking LLM Agents on Multi-Modal Biomedical Machine Learning Tasks |
| topic | Computational Engineering, Finance, and Science |
| url | https://arxiv.org/abs/2605.15766 |